{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "f = open('../resnet50_finetuning.log','r')\n",
    "text = f.read().split('\\n')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "res = []\n",
    "for line in text:\n",
    "   res.append(line.split('\\t'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "res = res[:len(res)-1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "y0 = []\n",
    "y1 = []\n",
    "for line in res:\n",
    "    y0.append(float(line[1])/10)\n",
    "    y1.append(float(line[2])/100)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x7faf1c7d12e8>"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "x = range(0,len(res))\n",
    "plt.plot(x,y0)\n",
    "plt.plot(x,y1)\n",
    "plt.legend(['train loss','train accuracy'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "f.close()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.10"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}
